Twitter®, Facebook®, Plurk®, and any number of other social networking websites are some of the most commonly visited sites accessible via the internet today. Considering the large number of users associated, social networking sites produce and/or process extraordinary amounts of data. Of course, individual visitors to social networking sites do so to review the posts, statuses, pictures, and videos, etc. of other users. While these sites are a rich source of information, only a certain amount of the information generated is relevant towards any given topic. Most of the remainder is personal information, very specific to individual users and not of any interest to an individual or computer seeking certain data. Searching for information is thusly a daunting task when data arrives at such high speed and in such volume.
Accordingly, a need exists for a system, method, and apparatus for automatic topic relevant content filtering from social media text streams using weak supervision.